Extracting the Fault Features of an Acoustic Emission Signal Based on Kurtosis and Envelope Demodulation of Wavelet Packets

2017 ◽  
pp. 101-111
Author(s):  
Li Lin ◽  
Qiang Xu ◽  
Yong Zhou
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Wavelet Packets ◽  
Emission Signal ◽  
Fault Features

Fault Diagnosis of Reciprocating Compressor Valve Using Acoustic Emission

2012 ◽  
Author(s):  
Y. F. Wang ◽  
X. Y. Peng
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Reciprocating Compressor ◽  
Flow Passage ◽  
Emission Signal ◽  
Fault Features ◽  
Discharge Valve ◽  
Valve Disc ◽  
Deviation Degree ◽  
Discharge Pressure

The faults of a reciprocating compressor valve can be diagnosed using the acoustic emission. Four typical valve faults including the crack, rupture and deformation in the valve discs and leakage through the flow passage were investigated. The fault features were extracted by comparing the acoustic emission signals from the failed valves with those from the normal valves. The results show that the feature locations where the discharge valve opened and closed could easily be identified by the envelope waveform of acoustic emission signal, and they changed when the valve failed including the rupture and deformation in valve discs and leakage through the flow passage and changed with the variation of the discharge pressure. The extent to which the valve failed could be estimated by the deviation degree between the opening/closing locations and the standard ones. The leakage caused by these valve faults could also lead to the increase in the amplitude of the acoustic emission wave. However, the fault of crack in valve disc couldn’t be identified by acoustic emission signal effectively.

Research on Acoustic Emission Test Method for Metal Fatigue Fracture Based on Wavelet Packets Feature Extraction

2011 ◽  
Vol 488-489 ◽  
pp. 432-435
Author(s):  
Qi Wang ◽  
Yin Sheng Chen ◽  
Kai Song
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Fatigue Crack ◽  
Crack Propagation ◽  
Fatigue Fracture ◽  
Fatigue Crack Propagation ◽  
Acoustic Emission Signal ◽  
Wavelet Packets ◽  
Metal Fatigue ◽  
Emission Signal

The appearance and growth of the microcracks in the structure is an important factor that influences the structure safety and its service life. Thus it is very important to detect the crack and monitor its growth at the beginning of the crack. Aiming at the main style of failures in metal structure - fatigue fracture, this paper research acoustic emission waveforms analysis that base on wavelet packets feature extraction, through processing acoustic emission signal to test metal fatigue fracture. First, this paper analyses the reason of metal fatigue fracture and introduces the theory of acoustic emission. Based on that, we establish the time domain module of acoustic emission signal and extract the feature of acoustic emission signal using wavelet packets. According to the experimental results bending specimen, acoustic emission techniques monitoring fatigue crack propagation is certificated not only to resemble variable rule of fatigue crack propagation but also to catch generation of fatigue crack in real time. Compared with the method of parameter extraction, this method can not only realize real-time and dynamic monitoring, but also get the result that is similar with fatigue crack expanding rate curve.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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